Incremental Joint Extraction of Entity Mentions and Relations

Abstract

We present an incremental joint framework to simultaneously extract entity mentions and relations using structured perceptron with efficient beam-search. A segment-based decoder based on the idea of semi-Markov chain is adopted to the new framework as opposed to traditional token-based tagging. In addition, by virtue of the inexact search, we developed a number of new and effective global features as soft constraints to capture the interdependency among entity mentions and relations. Experiments on Automatic Content Extraction (ACE) 1 corpora demonstrate that our joint model significantly outperforms a strong pipelined baseline, which attains better performance than the best-reported end-to-end system.

A Language-Independent Neural Network for Event Detection

“…(4) Joint Model (Li and Ji, 2014), a joint structured perception approach, incorporating multilevel linguistic features to extract event triggers and arguments at the same time so that local predictions can be mutually improved. (5) Pattern Recognition (Miao and Grishman, 2015), using a pattern expansion technique to extract event triggers.…”

“…The majority of existing methods regard this problem as a classification task, and use machine learning methods with hand-crafted features, such as lexical features (e.g., full word, pos tag), syntactic features (e.g., dependency features) and external knowledge features (WordNet). There also exists some studies leveraging richer evidences like cross-document (Ji et al, 2008), cross-entity (Hong et al, 2011) and joint inference (Li and Ji, 2014). Despite the effectiveness of feature-based methods, we argue that manually designing feature templates is typically labor intensive.…”

Joint Mention Extraction and Classification with Mention Hypergraphs

“…Neelakantan and Collins (2014) looked into the problem of automatically constructing dictionaries with minimal supervision for improved named entity extraction. Li and Ji (2014) presented an approach to perform the task of extraction of mentions and their relations in a joint and incremental manner.…”

Transition-Based Deep Input Linearization

“…Joint models have been explored for grammar-based approaches to surface realisation using HPSG and CCG (Carroll and Oepen, 2005;Velldal and Oepen, 2006;Espinosa et al, 2008;White and Rajkumar, 2009;White, 2006;Carroll et al, 1999). Joint models have been proposed for word segmentation and POS-tagging (Zhang and Clark, 2010), POS-tagging and syntactic chunking (Sutton et al, 2007), segmentation and normalization (Qian et al, 2015), syntactic linearization and morphologization , parsing and NER (Finkel and Manning, 2009), entity and relation extraction (Li and Ji, 2014) and so on. We propose a first joint model for deep realization, integrating linearization, function word prediction and morphological generation.…”